Clinical trials remain the foundation of medical innovation. But without addressing the operational and workforce challenges that underpin them, their full potential will remain out of reach. These challenges become even more acute as additional grant cancellations and funding cutbacks are announced.

Technology can help ease the burden by making clinical trial and research processes more efficient and by reducing barriers to participation. However, technology is not a substitute for the human relationships at the heart of clinical care. The path forward requires building systems that bring people and technology together, empowering each to do what they do best.

Clinical trials are foundational to developing new therapies and treatments for chronic, common, and rare diseases. They are the essential step in the 10 to 15-year journey from drug discovery to FDA-approval.

Trial timelines are often protracted due to significant challenges of enrolling patients and ensuring they don’t drop out. According to the Association of Clinical Research Professionals, 85% of trials fail to recruit enough patients, while 80% are delayed due to other recruitment factors.

Recent grant cancellations and clinical funding cutbacks exacerbate these challenges for hospitals, health systems, and academic medical centers conducting clinical trials and research. The latest round of cuts, detailed in a July 2026 Healthcare Innovation article, paints a deeply concerning picture: the Agency for Healthcare Research and Quality (AHRQ) is now effectively dismantled, with its grantmaking infrastructure largely shut down.

As funding disappears, hospital-based research teams must find new efficiencies. Now more than ever, these teams are pressured to reduce costs, tighten timelines and close resource gaps.

When I was studying public health over 20 years ago, I recall learning about the research to practice gap — the delay between scientific findings and changes in actual healthcare practice. A seminal study in 2011 found that there was an average delay of 17 years. When you combine this with the fact that it takes on average 10 to 15 years for a new drug to be discovered and approved, it’s hard not to be despaired. Working in healthcare over the last 15 years, I have witnessed firsthand the slow, manual and labor-intensive processes that burden both retrospective and prospective research.

Most of the healthcare industry has embraced technology to streamline operations and address similar challenges, so why does clinical research still lag far behind? This reality is further amplified by recent federal funding pressures and workforce shortages.

The challenge is not a lack of scientific innovation, but a failure to modernize how trials are executed and recognize the essential workforce steps involved amid dwindling grants and research funding opportunities.

The Overlooked Workforce Problem

I have seen firsthand the fulcrum around which all success in clinical trials rotates: the clinical research coordinator. Few health system leaders outside the field fully understand the importance of that role.

Clinical research coordinators are responsible for everything from identifying eligible patients to managing study protocols and ensuring regulatory compliance. Yet the role lacks a clearly defined career path. It is often a serendipitous route where recent biology graduates take jobs as research assistants at nearby institutions but quickly find limited upward trajectory and pay constraints.

There is a formal credential, the Certified Clinical Research Coordinator designation offered by the Association of Clinical Research Professionals. However, the position generally offers less structure, fewer incentives, and lower long-term retention compared to other clinical professions. Burnout among clinical research coordinators is commonplace.

In a 2022 OpenClinica survey, 61% of all clinical research staff reported being completely or somewhat burned out, and 52% of site respondents said their turnover rate had increased since 2020. The Society for Clinical Research Sites found that annual turnover rates for patient-facing clinical research professionals have risen to between 35 to 61%.

The result is a workforce that is both essential and insufficient, one that cannot scale to meet growing demand, and struggles with clinical research’s top challenges as mentioned above: identifying and managing trial participants.

The Bottleneck: Finding the Right Patients

The process to identify eligible patients for a clinical trial is still largely manual. This creates a significant bottleneck.

Coordinators or clinicians must sift through electronic health records (EHRs), reviewing patient charts one by one to determine eligibility based on complex inclusion and exclusion criteria. A mixed-methods study across 10 university hospitals found that 46% of interviewees said identifying suitable patients actively hampers routine care, and the screening procedure, searching for patients and checking all eligibility criteria, was identified as the single most time and labor-intensive step in recruitment.

Precision medicine adds another layer of complexity. Clinical trials often now require biomarker data, and sometimes multiple biomarkers, making it harder for clinical researchers to find the proverbial “needle in the haystack”. For example, the proportion of trials requiring the presence or absence of a specific genomic alteration increased more than five-fold between 2006 and 2013. By 2017, trials using biomarkers to stratify patients constituted 34% of industry-sponsored oncology trials.

These inefficiencies have real consequences. Around 80% of trials fail to meet the initial enrollment target and timeline, and 23% of patients enrolled don’t complete the trial. The saddest reality of missed identification is that potentially eligible patients are never given the opportunity to participate. According to a 2020 HINTS study, 41% of U.S. adults were not even aware that clinical trials exist for their condition.

Technology offers a clear opportunity to address these challenges. Advanced data tools can build inclusion and exclusion criteria directly on top of a data warehouse to streamline patient identification.

For example, sophisticated software allows institutions to find patients faster. Instead of starting with a list of 1,000 patients to review manually, clinical research coordinators begin with a shortlist of 100 highly relevant candidates. Some health systems even deploy remote clinical staff to validate eligibility through a hybrid approach to supplement their understaffed, in-house terms.

Collaborative Intelligence is Critical

There is no doubt that clinical trials in the U.S. would benefit from the new technological capabilities mentioned above. However, automation has its limits in this corner of the health ecosystem. Human interaction is the decisive factor in clinical trial enrollment and success.

Patients contemplating participation in a clinical trial are commonly facing serious, life-threatening conditions. They may be at the most vulnerable point in their lives. A human touch remains essential to the entire process.

While a bot might be able to text a patient to see if they are interested, having a human explain the nuances of an experimental treatment should never be automated. The most effective model for clinical trial automation features collaborative intelligence, where best-in-class technology is leveraged to streamline data-intensive tasks while clinical experts remain in the loop to validate findings and handle patient interactions. In this scenario, technology absorbs the administrative workload so clinical staff can focus on the patient relationships that make trials work.

This balance is critical, as over-reliance on automation risks eroding patient trust, while underutilization leaves inefficiencies untouched.

More, Not Less, Clinical Trials Ahead

Market projections expect the U.S. clinical trials market to grow to $99.25 billion by 2033, with a compound annual growth rate of 6.07% over the next decade. To truly scale clinical research during these difficult times for the profession, we must invest in both infrastructure and people.

The collaborative intelligence approach creates clearer pathways for clinical research professionals and ensures new tools are implemented in ways that support human expertise, rather than replace it.

As President of Curation Solutions, Lidia Bernik oversees MRO’s strategy and operations focused on advancing data curation, clinical insights, and scalability across healthcare and life sciences. She brings more than 20 years of experience driving innovation, operational excellence, and growth, all grounded in a deep commitment to improving patient care. Before joining MRO, Lidia served as General Manager of the Real-World Data Business at Flatiron Health, where she led a 2,000-person team and expanded its oncology data platform from 70,000 to more than 1.7 million patients. Her background also includes research and administration roles at NYC Health + Hospitals, the Mount Sinai Health System and Weill Cornell Medicine as well as work in the nonprofit sector advancing suicide prevention and crisis response. Lidia earned her Bachelor of Science in Human Service Studies from Cornell University, a Master of Health Science from Johns Hopkins Bloomberg School of Public Health, and an MBA in Healthcare Administration from Baruch College. She is certified in Public Health and holds a certificate in Health Information Technology from Columbia University, and is a fellow of the American College of Health Data Management.